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Is Deep Learning the same as Machine Learning?
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Machine Learning vs Deep Learning: Exploring Their Differences

  • November 14, 2024
  • Tolulope Victoria
  • 0

 Deep Learning and Machine learning are words often used interchangeably. This has led many to assume that they mean the same thing. While these two concepts work together, they still have their distinct meaning and unique features. We have established that Deep learning is a subset of Machine Learning in our last blog post. We also examined some of the key components in Deep learning and how it is being applied across industries. You can read more on Deep learning to gain better understanding.

However, we are yet to answer a major question in the hearts of many. Is Deep Learning the same as Machine Learning? If not How do they differ? If yes What are some of the similarities that establish their sameness? This will be our focus in this blog post. Join us on this journey to explore the differences between these two concepts in Artificial Intelligence.

Differences Between Machine Learning and Deep Learning

To begin with, a simple way to understand this is to see Artificial Intelligence as the broad mother of Machine learning with Deep learning being a part of this same family. Before examining the key differences between DL and ML it is important to establish this foundational difference. Under the broad category of Artificial Intelligence lies Machine Learning and under the Machine Learning category lies Deep Learning. This can be likened to these concepts them overlapping each other in a subset. Having established this foundation, below are some other differences to consider.

Data Structure 

While both Machine Learning and Deep Learning require datasets for quality training . DL models require larger datasets than ML models. DL models are known to work with millions of data points to provide more accurate results while ML models work with thousands of data points. The multiple layered structure embedded in Deep Learning models enable them to give more accurate results while carrying out complex tasks.

Additionally, the data structure utilized in Deep learning models differ from that of ML models as they work with multiple neural networks intertwined together. While ML models work mainly with identifying patterns from structured data, Deep Learning models are more suitable in unstructured data. They require the identification of complex relationships  between unstructured data objects like image classification and speech recognition.

Human Intervention 

Since the need for Deep learning evolved from the need to have computers simulate human brain structure, in being able to carry out tasks, predict patterns with little or no human intervention. An obvious difference between ML models and DL models is in the level of human involvement required. DL models require less human intervention as they are capable of learning from their environment and making decisions from past mistakes. ML models on the other hand require continual human intervention to drive its results.  

However, ML models are easier to interpret because they work with simpler mathematical models. DL is more difficult to analyze by humans because of the mathematical model’s complexity.

Training Time and Method

Machine Learning models require shorter time to train and set up due to its smaller size. Although their effectiveness is often limited when compared to DL models. Deep learning models on the other hand require longer time to train as they work with big data points. They are better suited to produce better accuracy, especially because the quality can improve over time.

Machine learning works primarily with four training methods: Supervised learning, Unsupervised learning, Semi-supervised Learning and Reinforcement Learning. DL methods on the other hand require more co plenty training methods such as convolutional neural networks, recurrent neural networks amongst others.

Computational Power

Deep learning models are more complex than ML models. As such, this complexity in its learning demand causes it to require more computational power and higher storage compared to ML models. ML models often require quality clusters and other infrastructural requirements for successful training and set up. Invariably, this means that  Deep learning models demand higher resources and cost more than ML models. 

Complexity And Performance 

Another way in which ML differs from DL is in the level of complexity required. Machine Learning performs best for tasks that do not require much complexity. Deep Learning on the other hand is more suited for complex tasks such as image recognition because of its ability to identify errors or abnormalities that might not be obvious to humans.

Because of the complexity of its mathematical models, the results of DL models are often difficult to analyze and explain. On the contrary, the results from an ML model are simpler to analyze.

In terms of application, ML models are widely applied already across industries to carry out simpler tasks. Deep learning is better suited for more complex tasks like speech recognition, image recognition and object detection. The essence of deep learning is in how deeply the machine can learn to carry out more complex tasks in the similitude of the human brain.